Semester of Graduation
First Major Professor
Master of Science (MS)
Early disease detection is critical for the long-term well-being of the swine industry, as changes over the last several decades have left the industry vulnerable to costly outbreaks. Here we use a county-level spatiotemporal disease spread model to simulate the spread of disease over time and compare the disease detection performance of six sampling methods across a variety of settings. We find that spatially balanced sampling methods have a higher probability of detecting disease in early periods than benchmark methods such as simple random sampling under many settings, in large part because spatially balanced methods produce samples that are well spread throughout the population region. We present these simulation results in an interactive web app that will help users identify settings for which spatially balanced methods are expected to perform well. For each simulation setting, the app displays probabilities of detection for the methods under consideration at each time point as well as maps that visualize the spread of disease over time. Finally, the app allows users to select spatially balanced samples of their own for the regions examined in our simulation study. Overall, we seek to convince readers of the merits of using spatially balanced sampling for the purpose of detecting disease as well as provide an intuitive understanding of the methods themselves and when and why we expect them to perform well.
Embargo Period (admin only)
Morris, Paul, "Introducing the use of spatially balanced sampling for livestock disease detection through an interactive web app" (2020). Creative Components. 666.